## What is Config?

Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.

## How to Define Config

The config is defined as a Python dictionary with two main keys:
- `embedder`: Specifies the embedder provider and its configuration
  - `provider`: The name of the embedder (e.g., "openai", "ollama")
  - `config`: A nested dictionary containing provider-specific settings

## How to Use Config

Here's a general example of how to use the config with mem0:

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "embedder": {
        "provider": "your_chosen_provider",
        "config": {
            # Provider-specific settings go here
        }
    }
}

m = Memory.from_config(config)
m.add("Your text here", user_id="user", metadata={"category": "example"})
```

## Why is Config Needed?

Config is essential for:
1. Specifying which embedding model to use.
2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
3. Ensuring proper initialization and connection to your chosen embedder.

## Master List of All Params in Config

Here's a comprehensive list of all parameters that can be used across different embedders:

| Parameter | Description |
|-----------|-------------|
| `model` | Embedding model to use |
| `api_key` | API key of the provider |
| `embedding_dims` | Dimensions of the embedding model |
| `http_client_proxies` | Allow proxy server settings |
| `ollama_base_url` | Base URL for the Ollama embedding model |
| `model_kwargs` | Key-Value arguments for the Huggingface embedding model |
| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model |
| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file for VertexAI |


## Supported Embedding Models

For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
